

'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class    snake_case     (      snake_case_		):






				"""simple docstring"""
				_a    	=     42
				_a    	=     42



				def __init__(    self,     _lowercase,     _lowercase       )     ->							int:
											super().__init__()
											self.register_modules(unet=_lowercase,     scheduler=_lowercase       )





				@torch.no_grad()
				def __call__(    self,     _lowercase = 1,     _lowercase = 2000,     _lowercase = None,     _lowercase = "pil",     _lowercase = True,     **_lowercase,     )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				self.unet.config.sample_size
											SCREAMING_SNAKE_CASE_				=				(batch_size, 3, img_size, img_size)

											SCREAMING_SNAKE_CASE_				=				self.unet

											SCREAMING_SNAKE_CASE_				=				randn_tensor(_lowercase,     generator=_lowercase       ) * self.scheduler.init_noise_sigma
											SCREAMING_SNAKE_CASE_				=				sample.to(self.device       )

											self.scheduler.set_timesteps(_lowercase       )
											self.scheduler.set_sigmas(_lowercase       )

											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps       )       ):
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.sigmas[i] * torch.ones(shape[0],     device=self.device       )

																		# correction step
																		for _ in range(self.scheduler.config.correct_steps       ):
																									SCREAMING_SNAKE_CASE_				=				self.unet(_lowercase,     _lowercase       ).sample
																									SCREAMING_SNAKE_CASE_				=				self.scheduler.step_correct(_lowercase,     _lowercase,     generator=_lowercase       ).prev_sample

																		# prediction step
																		SCREAMING_SNAKE_CASE_				=				model(_lowercase,     _lowercase       ).sample
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.step_pred(_lowercase,     _lowercase,     _lowercase,     generator=_lowercase       )

																		SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				output.prev_sample, output.prev_sample_mean

											SCREAMING_SNAKE_CASE_				=				sample_mean.clamp(0,     1       )
											SCREAMING_SNAKE_CASE_				=				sample.cpu().permute(0,     2,     3,     1       ).numpy()
											if output_type == "pil":
																		SCREAMING_SNAKE_CASE_				=				self.numpy_to_pil(_lowercase       )

											if not return_dict:
																		return (sample,)

											return ImagePipelineOutput(images=_lowercase       )






"""simple docstring"""



import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible






class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.bin",
            "safety_checker/model.safetensors",
            "vae/diffusion_pytorch_model.bin",
            "vae/diffusion_pytorch_model.safetensors",
            "text_encoder/pytorch_model.bin",
            "text_encoder/model.safetensors",
            "unet/diffusion_pytorch_model.bin",
            "unet/diffusion_pytorch_model.safetensors",
        ]
        self.assertTrue(is_safetensors_compatible(lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "unet/diffusion_pytorch_model.bin",
            "unet/diffusion_pytorch_model.safetensors",
        ]
        self.assertTrue(is_safetensors_compatible(lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.bin",
            "safety_checker/model.safetensors",
            "vae/diffusion_pytorch_model.bin",
            "vae/diffusion_pytorch_model.safetensors",
            "text_encoder/pytorch_model.bin",
            "text_encoder/model.safetensors",
            "unet/diffusion_pytorch_model.bin",
            # Removed: 'unet/diffusion_pytorch_model.safetensors',
        ]
        self.assertFalse(is_safetensors_compatible(lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "text_encoder/pytorch_model.bin",
            "text_encoder/model.safetensors",
        ]
        self.assertTrue(is_safetensors_compatible(lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.bin",
            "safety_checker/model.safetensors",
            "vae/diffusion_pytorch_model.bin",
            "vae/diffusion_pytorch_model.safetensors",
            "text_encoder/pytorch_model.bin",
            # Removed: 'text_encoder/model.safetensors',
            "unet/diffusion_pytorch_model.bin",
            "unet/diffusion_pytorch_model.safetensors",
        ]
        self.assertFalse(is_safetensors_compatible(lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.fp16.bin",
            "safety_checker/model.fp16.safetensors",
            "vae/diffusion_pytorch_model.fp16.bin",
            "vae/diffusion_pytorch_model.fp16.safetensors",
            "text_encoder/pytorch_model.fp16.bin",
            "text_encoder/model.fp16.safetensors",
            "unet/diffusion_pytorch_model.fp16.bin",
            "unet/diffusion_pytorch_model.fp16.safetensors",
        ]
        __a       =    "fp16"
        self.assertTrue(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "unet/diffusion_pytorch_model.fp16.bin",
            "unet/diffusion_pytorch_model.fp16.safetensors",
        ]
        __a       =    "fp16"
        self.assertTrue(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)


 def 						a__   ( self						):
        # pass variant but use the non-variant filenames
        __a       =    [
            "unet/diffusion_pytorch_model.bin",
            "unet/diffusion_pytorch_model.safetensors",
        ]
        __a       =    "fp16"
        self.assertTrue(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.fp16.bin",
            "safety_checker/model.fp16.safetensors",
            "vae/diffusion_pytorch_model.fp16.bin",
            "vae/diffusion_pytorch_model.fp16.safetensors",
            "text_encoder/pytorch_model.fp16.bin",
            "text_encoder/model.fp16.safetensors",
            "unet/diffusion_pytorch_model.fp16.bin",
            # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
        ]
        __a       =    "fp16"
        self.assertFalse(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "text_encoder/pytorch_model.fp16.bin",
            "text_encoder/model.fp16.safetensors",
        ]
        __a       =    "fp16"
        self.assertTrue(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)


 def 						a__   ( self						):
        # pass variant but use the non-variant filenames
        __a       =    [
            "text_encoder/pytorch_model.bin",
            "text_encoder/model.safetensors",
        ]
        __a       =    "fp16"
        self.assertTrue(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)






 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.fp16.bin",
            "safety_checker/model.fp16.safetensors",
            "vae/diffusion_pytorch_model.fp16.bin",
            "vae/diffusion_pytorch_model.fp16.safetensors",
            "text_encoder/pytorch_model.fp16.bin",
            # 'text_encoder/model.fp16.safetensors',
            "unet/diffusion_pytorch_model.fp16.bin",
            "unet/diffusion_pytorch_model.fp16.safetensors",
        ]
        __a       =    "fp16"
        self.assertFalse(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)
